22 Jun 2026

ML Engineer

Kogan.com

South Melbourne

22 Jun 2026

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ML Engineer

Kogan.com seeks a Data/ML Engineer to design and run data and ML pipelines for marketing, purchasing, logistics, and finance. The role involves developing production ML systems, MLOps, and LLM applications, requiring strong Python and cloud experience.

Core AIOn-siteFull-timeMid LevelPythonMachine Learning

ML Engineer

Apply

Core AIOn-siteFull-timeMid LevelPython

Salary

Not specified

Work Location

South Melbourne, Victoria, Australia, AU

Work Model

On-site

Employment Type

Full-time

Experience Level

Not Applicable

Core Qualifications

Technical (Must-have)

PythonMachine LearningMLOpsSQLGoogle Cloud PlatformVertex AIBigQueryCloud RunGitCI/CDLLMsEmbeddingsVector DatabasesRAGAI Agents

Soft Skills

Problem SolvingCollaborationOwnershipAutonomy

Preferred Qualifications

Technical (Nice-to-have)

Recommendation SystemsSearch RankingForecastingDatabricksSageMakerKubeflowApache KafkaPub/Sub

Key Responsibilities

•Design, build, and deploy machine learning models for recommendation, forecasting, segmentation, churn, pricing, fraud detection
•Build reliable and scalable machine learning services for real-time and batch predictions
•Develop and maintain MLOps pipelines for automation of model training, validation, deployment, monitoring, retraining
•Work with large datasets to create robust feature pipelines and reusable datasets
•Design, evaluate, and deploy AI solutions using LLMs, retrieval systems, agents
•Implement monitoring frameworks to track model performance, drift, accuracy, business impact
•Partner with Product Managers, Engineers, Analysts to identify ML opportunities
•Develop solutions with software engineering best practices including Git, CI/CD, testing, observability
•Contribute to experiments with AI and emerging technologies

ML EngineerData EngineerMachine LearningMLOpsPythonGCPeCommerceRetailLLMAI

Key Responsibilities

•Design, build, and deploy machine learning models for recommendation, forecasting, segmentation, churn, pricing, fraud detection

•Build reliable and scalable machine learning services for real-time and batch predictions

•Develop and maintain MLOps pipelines for automation of model training, validation, deployment, monitoring, retraining

•Work with large datasets to create robust feature pipelines and reusable datasets

•Design, evaluate, and deploy AI solutions using LLMs, retrieval systems, agents

•Implement monitoring frameworks to track model performance, drift, accuracy, business impact

•Partner with Product Managers, Engineers, Analysts to identify ML opportunities

•Develop solutions with software engineering best practices including Git, CI/CD, testing, observability

•Contribute to experiments with AI and emerging technologies